4 Standards Opportunities for Smart Manufacturing

Most standards for manufacturing created in the last 30 years have already achieved a high degree of maturity; however, to enable SMS, further standards development is necessary. We identify several areas in the SM Ecosystem where standards can be extended or where new standards should be developed, and we identify some new initiatives focused on SMS that will spur the development of both SMS technology and standards.

4.1 Standards Needs

Full realization of SMS capabilities will require replacement of the classical manufacturing system architectural paradigm based on a hierarchical control model [45]. Figure 6 shows a new paradigm based on distributed manufacturing services, also called Cyber Physical Production Systems (CPPS).⁸ The paradigm shift is made possible by the introduction of smart devices accessible as services on a network, more embedded intelligence at every level, predictive analytics that enable responsive control, and cloud technology that enables virtualization of control and engineering functions at all hierarchical levels. With these capabilities in place, widespread automation across hierarchical levels using new approaches to control is a realistic possibility.

The new service-oriented paradigm ultimately transforms the smart manufacturing ecosystem into a fully connected and integrated system, shown in Figure 7. All the manufacturing functions along three dimensions and in the manufacturing pyramid can be virtualized and hosted as services, except those time-critical and safety-critical manufacturing functions remaining at the shop floor level.

Existing manufacturing standards are far from being sufficient for the service-oriented smart manufacturing ecosystem. Areas that need new standards support include reference architecture, cybersecurity, factory networking, supply chain integration, and data transfer from factory floor to enterprise level. Table 16 lists these standards’ opportunities and the types of capabilities they support. Specifically, new or improved standards in these areas would improve capabilities associated with agility (A), quality (Q), productivity (P) and sustainability (S). The first column is the area of opportunity for new standards. The second column shows where the standards impact the SM Ecosystem—Product Lifecycle (PL), Production System Lifecycle (PSL), Business Cycle (BC), and Smart Production Pyramid (SPP). The third column shows how the standards map to SMS capabilities. Note that we present this not as a complete list, but rather as a starting point for exploration and discussion of the infrastructure of SM standards.

As shown in the table, a high-level reference architecture for SMS, including functional models and architectural definitions, is needed to integrate functions within and across the extended enterprise, including between suppliers and customers. These models will form the basis for dynamic production capabilities and customization of end products.

Information models representing smart devices on the shop floor and manufacturing services are also needed to increase productivity and agility by supporting reconfiguration of equipment, as well as allowing more optimal health maintenance. A reference architecture for CPPS will enable development of production modules incorporating smart devices. As these systems of systems come into place, intelligent machine communication standards along with an architectural framework will allow automation of system-level controls and transparency of data from the lowest levels of manufacturing to higher control levels.

This increase of automation possibilities brings a need for new types of interfaces for humans to interact with the machines. Much of the performance data for individual machines can be presented to people through dashboards that also enable direct control. Similarly, dashboards for monitoring and controlling system-wide performance are needed. Optimization of these interfaces is an area of active research, and related standards should accordingly follow. ISA formed an HMI committee to establish standards, recommend practices, and provide technical reports relating to human-machine interfaces (HMIs) in manufacturing and processing applications.

In addition, for production system design, operational data from manufacturing is needed to generate new designs and better process plans more quickly. Although it is an area of research, no explicit standards yet exist to assess production system capabilities and to link the results back to upstream activities in the lifecycle.

For product lifecycle management, AMP 2.0 [5] recommends an ontology of data and artifacts that captures, stores, visualizes, searches, and shares both static and dynamic data, both along the product lifecycle and through the supply chain. The development of such a standard will enable more agility in the supply chain and reuse of products designs for rapid redesign.

Product lifecycle data combined with data from manufacturing processes can enable advanced analyses of the processes themselves, resulting in process improvement in terms of productivity, sustainability, and quality. For instance, analysis of product performance in the field can sometimes reveal quality issues in production.

One vision for SMS is that products themselves can contain the history of how, when, and where they were manufactured. The MTConnect Institute is starting standards activities that will enable this type of traceability. Technology and standards for big data and cloud manufacturing will allow many types of advanced analysis and other functions to be provided on a service basis, thereby making them more readily accessible to manufacturers.

Standards related to sustainability evaluation for manufacturing systems are evolving along each of the dimensions described. Current practices for sustainability evaluation for manufacturing follow the Life Cycle Assessment (LCA) methodology standardized in the ISO 14000 series on environmental management. These standards operate from a management perspective and use a top-down approach to estimating sustainability impacts of different processes involved in goods production. In SMS, we envision more accurate measures of the sustainability impacts of each of the manufacturing processes based on measures of operational data for each process. These measures will allow more accurate accounting of the impacts of individual decisions at each production facility. Still, many challenges will exist since sustainability assessment, by its very nature, must address tradeoffs between many criteria. How this data can be used along each of the dimensions of the SMS ecosystem and how sustainability impacts are apportioned to the different aspects of production and the product are grand challenges for sustainability assessment. Standards are necessary to provide unambiguous and comparable data to support this decision-making process.

4.2 New Initiatives

Most of the standards areas that we described are being extended to address SMS capabilities. Quite a few new initiatives worldwide have emerged to contribute to the standards and opportunities identified above.

4.2.1 Industrie 4.0

Industrie 4.0 is a key initiative in Germany containing a technical strategy for achieving SMS. The enablers of Industrie 4.0 are the internet, mobile computing, and cloud computing technologies. A goal of Industrie 4.0 is the creation of innovations including smart products, smart production systems, smart factories, and smart logistics running in a decentralized and dynamic fashion [56]. The Industry 4.0 working group recommended standardization and open standards for a reference architecture as the first priority for implementation [57]. Following this recommendation, the German Commission for Electrical, Electronic & Information Technologies (DKE) produced a standardization roadmap in 2014 [17]. In parallel, Platform Industrie 4.0 projects were established by a number of German associations to form interdisciplinary working groups on issues for future standardization. The result is the Reference Architectural Model (RAMI) 4.0 and the Industrie 4.0 components [58] that describe functional models for CPPS. These will serve industry as a basis for developing future products and business models in Germany.

4.2.2 Internet of Things(IoT)

In the area of the Internet of Things (IoT), the Europe Union (EU) founded several projects to develop an IoT reference model and reference architecture. IoT-A, an EU Seventh Framework Project, created an architectural reference model envisioned as a foundation for the Internet of Things [62]. IoT@Work is another EU project led by Siemens AG that focuses on harnessing IoT technologies in industrial and automation environments [63]. Three main scenarios providing requirements for the IoT@Work architecture include agile manufacturing, large- scale manufacturing, and remote maintenance.

In the U.S., the Industrial Internet Consortium (IIC) [36] founded by GE, IBM, CISCO, Intel, and AT&T is a transatlantic cousin of Industrie 4.0. IIC is concerned with anything that can be connected to the internet, provide data as feedback, and raise efficiency. Its scope is larger than Industrie 4.0 in that it addresses not only manufacturing systems, but also energy, healthcare, and infrastructure. Unlike Industrie 4.0, which works on standards directly, IIC has set a goal to “define and develop the reference architecture and frameworks necessary for interoperability” and which might help set future standards. Table 17 shows a comparison between Industrie 4.0 and IIC from [60].

Meanwhile, the Open Interconnect Consortium (OIC), founded by leading technology companies like Samsung, Cisco, GE, and Intel, is proposing an open-source solution to enable device-to-device connectivity for IoT [61]. OIC focuses on building a common communications standard and sponsors the IoTivity project to build an open- source reference implementation of those specifications. The adoption of the OIC standard is expected to begin in consumer electronics and expand over time to industrial applications.

Open Machine communication standards are one of the key enablers of IoT implementation. The diversified IoT use scenarios mean that there will be no single ‘winner’ in terms of Machine-to-Machine (M2M) standards. Initiatives such as OneM2M , HyperCat , OMA LightweightM2M , Eclipse M2M and Weightless [70] have potential to be de facto M2M standards[65]. Specifically, Eclispe SCADA will provide connectivity to a variety of industrial devices and offer a monitoring system to create alarms and events and record historical data and a framework to build custom user interfaces and visualizations for those functions[71]. A new ETSI(European Telecommunications Standards Institute) Technical Committee is also developing standards for M2M Communications in cellular segment for IoT applications in industrial automation, health care, and supply chains[64].

4.2.3 Cyber Physical System(CPS)

While the IoT deals with unique, identifiable, and internet-connected physical objects, cyber-physical systems efforts are concerned with the nature of cyber-physical coupling and the system of systems characteristics of software-controlled systems. Standards for CPS include a reference architecture, common services and functional models, semantics, security and safety standards, and standard interfaces for system-to-system interactions. A public working group led by NIST is working on terminology and a reference architecture for CPS [72]. CPS research and standards development are being worked on in multiple NIST Laboratories in programs on advanced manufacturing, cybersecurity, buildings and structures, disaster resilience, and smart grid. NIST efforts include work on Industrial Control Systems (ICS) as well. In Europe, the EU has invested significantly in CPS through its ARTEMIS and ECSEL JU programs and Smart CPS projects under the Horizon 2020 plan [73]. The Association for German Engineers founded Technical Committee 7:20 - Cyber-Physical Systems to support standards development in CPS from the perspective of automation technology [74].

4.2.4 Big Data and Cloud Manufacturing

The amount of data in manufacturing systems is exploding. Big-data analytics enables continuous innovation and process improvement of manufacturing systems, and has been recognized as a key enabler of SMS [80]. With a cloud-computing infrastructure, manufacturers gain the ability to access software and real-time data at lower cost and to respond quicker to customer issues. The IEEE Standards Association has introduced a number of standards related to big-data and cloud applications, including IEEE 2200-2012, IEEE 6136, and IEEE P2302. ISO/IEC JTC 1 recognized data analytics as an important future area for focus and established a Study Group on Big Data to identify standards gaps and propose standardization priorities to serve as a basis for future JTC 1 work [76]. NIST established a public working group to propose a reference architecture and identify standards related to Big Data, a fundamental technology for SMS [42]. While technology development in this area will have a huge impact on manufacturing, none of these activities are specifically directed at manufacturing. In May 2015, NIST and OAGI jointly held a Workshop on Open Cloud Architectures for Smart Manufacturing [78].

4.2.5 Smart Manufacturing Initiatives in the U.S.

While most of the existing consortia and professional societies in the U.S. are addressing SMS in some ways, several industrial consortia formed to address broader, overarching, needs of SMS. The oldest of these is the Smart Manufacturing Leadership Coalition (SMLC), a non-profit organization committed to the creation of a scaled, shared, infrastructure called the Smart Manufacturing Platform [75]. SMLC activities will help set future standards in integrating SM applications. Subsequently, the U.S. government initiated a series of institutes to support U.S. manufacturing. These institutes collectively called the National Network of Manufacturing Institutes, or NNMI, address different challenge areas for advanced manufacturing. The Digital Manufacturing and Design Innovation Institute (DMDII) most closely aligns with the SMS needs for information flow throughout an enterprise to enable the SMS capabilities—agility, quality, productivity, and sustainability. DMDII has issued three rounds of project calls in areas of strategic importance, including intelligent machine communication standards and cyber-physical manufacturing operating systems. In 2014, the U.S. Department of Energy announced intention to create another institute for clean-energy manufacturing based on smart manufacturing technology, including advanced sensors, controls, platforms, and modeling technology for energy efficiency.

The National Institute of Standards and Technology (NIST) has several initiatives addressing Smart Manufacturing. NIST is heavily engaged in efforts to develop new standards for the Digital Thread [39], Model- Based Enterprise [40], smart manufacturing design and analysis [95], additive manufacturing [97] and robotics [96]. NIST leads an effort to define requirements eventually leading to standards for cloud-based services for manufacturing. NIST work on cyber security for supply chains and industrial systems will have great importance for manufacturers [43]. Finally, NIST coordinates the networking of the NNMIs within the U.S. [44].

4.2.6 SDO Smart Manufacturing Related Activities

Various SDO activities are starting to focus explicitly on the needs for and impacts of the technologies fundamental to SMS—IoT, cloud computing, Big Data, and analytics. To help ensure the existence of adequate standards support for SM, in 2014 the IEC Standardization Management Board (SMB) set up a new Strategic Group, SG 8: Industry 4.0 – Smart Manufacturing. Its scope includes defining terminology, summarizing existing standards and standardization projects in progress, and developing a common strategy for implementation of smart manufacturing [59]. SG 8 will also foster relationships between IEC (TC3, TC 65) and institutions like ISO (TC 184), ISA, and IEEE on SM standards development. In 2015, the ISO Technical Management Board (TMB) passed a resolution to form an ISO/TMB Strategic Advisory Group on Industry 4.0/Smart Manufacturing. The SAG is tasked to provide a definition of, and give an overview on, available standards, use cases, and current work related to Industry 4.0/Smart manufacturing; to identify possible gaps where additional standards are needed; and to make recommendations on actions to be taken by TMB [82]. In the Fall of 2014, MESA launched the Smart Manufacturing Working Group to better orchestrate their projects related to Smart Manufacturing. Outputs from this group will include things such as expansion of MESA's 'Collaborative Manufacturing Dictionary' and a library of 'Manufacturing Business Processes' and 'Use Cases' that map production processes across internal operating departments and supply chains [74]. OAGI also established a Smart Manufacturing working group to develop multi-tiered supply chain collaboration guidelines and standards for engineered components to improve cost, quality, agility and more. Similarly, ASTM has established a Smart Manufacturing Advisory Board to guide their efforts.

4.2.7 Sustainable Manufacturing Standards

Typically, sustainability is discussed from three perspectives: environment, economic, and social. The focus of our study is on data and information that can be collected by a manufacturing organization rather than on organizational policies and practices. In 2008, ASTM formed a committee on Sustainability and subsequently formed a subcommittee specifically addressing Sustainable Manufacturing. While the standards of this subcommittee are not yet complete, we expect an initial set on the near-term horizon for enabling analysis of how manufacturing systems are impacting sustainability and can be improve in this respect. A focus of the ASTM standards is on characterizing manufacturing processes for environmental sustainability assessment. Sustainability is inherently a complex area in which multiple tradeoffs must be considered. In order to evaluate those trade-offs, accurate data reflecting the impact of individual activities and processing leading to the creation of some good or service is necessary. Until now, such data was very difficult and costly to obtain. Direct measure of the use of physical resources is now quantifiable and thus the focus of the standardization activities. In addition, ISO 22400 has initiated an addendum standard on KPIs for energy management specifically. A wide range of other activities focuses on assessing social factors related to sustainability. These include organizational practices and policies and do not fall within the scope of this study. Economic aspects of sustainability are also not specifically addressed, but the data gathered for SMS will be ultimately useful in these assessments as well. For instance, when trying to understand issues of resource efficiency, one must take an economic viewpoint and factor that against measures of resource utilization.

⁸. Smart devices are at the core of the area of technology development that has become known as Cyber Physical Systems, or CPS, of which CPPS is a part. ↩